Justin Gottschlich

Principal Scientist & Director/Founder of Machine Programming Research (Intel Labs)

Steering Committee Chair, ACM SIGPLAN Machine Learning and Programming Languages (MAPL)

Chair of Industrial Board and Executive Director at PRECISE, University of Pennsylvania

Principal Investigator and Co-Founder, Intel/NSF CAPA Research Center

Adjunct Assistant Professor, University of Pennsylvania (my Penn website)

HIGHLIGHTS

Invited machine programming talk @ NeurIPS 2020, ML for Systems Workshop: "A Glimpse Into Machine Programming @ Intel Labs" (coming soon)

Invited machine programming talk @ UWisc: "Machine Programming: Challenges and Opportunities" (video)

Our code similarity system, MISIM, has received wide coverage by MIT Technology Review, VentureBeat, ZDNet, The Singularity, The Register, and around 70 other venues. Our research has also been highlighted by wonderful venues like Communications of the ACM, New York Times, SDTimes, Economic Times, Venturebeat, and Wharton.

Keynote @ PRECISE's Industry Day: "Machine Programming: The Future of Autonomy"

Intel Newsroom Press Release on my team's Machine Programming Research.

CURRENT STUDENTS

Maaz Ahmad (advised by Alvin Cheung @ Berkeley)

Roshni Iyer (advised by Yizhou Sun and Wei Wang @ UCLA)

Ramneet Kaur (co-advised with Insup Lee @ Penn)

Celine Lee (advised by me and Dan Roth @ Penn)

Fangke Ye (advised by Vivek Sarkar @ Georgia Tech)

PanteA Zardoshti (advised by Mike Spear @ Lehigh)

RECENT COMMITTEES

USENIX ATC'21, ICLR'21, MLSys'21, OOPSLA'21, NeurIPS'20, MAPL'20 (SC chair), JPDC'20, aiDM '20, TheWebConf'20, MLSys'20, PACT'19 (SRC), SysML'19, MAPL'18 (general chair), MAPL'17 (PC chair)

Contact: justin.gottschlich@intel.com

BRIEF BIO

I founded and lead the Machine Programming Research group at Intel Labs. Machine programming (MP) is a new field of research that uses automation to improve software development productivity (e.g., the time it takes a developer to write code) and quality (e.g., performance, correctness, security, maintainability, etc.). We generally consider MP as a fusion of machine learning and formal methods, which rely heavily on programming languages and systems. We provide a brief overview of MP in our “Three Pillars of Machine Programming” vision paper (see Armando Solar-Lezama's website for a deeper dive). In academia, I have appointments as the industrial advisory board chair and executive director for the PRECISE Center at the University of Pennsylvania (Penn). I am also an adjunct assistant professor at Penn in the Computer and Information Science Department.

I have a deep desire to build bridges with thought leaders across industry and academia to identify disruptive research and push it forward as a community. Recently I have been working with Amazon, Brown, Georgia Tech, Google AI, Hebrew University, IBM Research, Microsoft Research, MIT, Penn, Stanford, Texas A&M, UC-Berkeley, and UCLA, to name a few. I co-founded and am the principal investigator of the joint Intel/NSF CAPA research center which focuses on simplifying the software programmability challenge for heterogeneous hardware. I also helped create the ACM SIGPLAN Machine Learning and Programming Languages workshop and currently serve as its steering committee chair. I have the distinguished honor of serving on the advisory board of Solar-Lezama et al.’s 2020 NSF Expeditions “Understanding the World Through Code.”

I have around 70 peer reviewed publications and issued patents with around 100 patents pending. I've given several dozen research talks at wonderful places like Berkeley, BMW, IBM Research, Penn, Stanford, UCLA, University of Washington, VMWare, and Wharton.

My (extremely dated) CV is here. Google scholar.

RECENT ACTIVITY

Invited machine programming talk @ NeurIPS 2020, ML for Systems Workshop: "A Glimpse Into Machine Programming @ Intel Labs" (coming soon)

Paper accepted to 2020 NeurIPS Computer-Assisted Programming Workshop "Software Language Comprehension using a Program-Derived Semantics Graph"

Paper accepted to NeurIPS 2020 ML for Systems Workshop: "ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures"

37th patent issued: "Methods and apparatus to detect anomalies of a monitored system" (10,802,942)

Invited MP talk @ UWisc: "Machine Programming: Challenges and Opportunities"

36th patent issued: "Programmable coarse grained and sparse matrix compute hardware with advanced scheduling" (10,769,748)

Keynote @ Department of Energy's Program Synthesis for Scientific Computing: "Machine Programming: Challenges and Opportunities"

Patent issued: "Neural Network Scheduling Mechanism" (10,719,760)

CACM article on machine programming: "Your Wish is My CMD" by Neil Savage

Paper accepted to MAPL 2020: "Learned Garbage Collection" (joint with MIT)

Paper accepted to CAV 2020: "An Abstraction-Based Framework for Neural Network Verification"

Patent issued: "Detecting Mobile Device Sensor Malfunctions" (10,591,313)

SDTimes & Economic Times highlighting our research.

Patent issued: "Extend GPU/CPU coherency to multi-GPU cores" (10,521,349)

Venturebeat has published an article on my team's machine programming research @ NeurIPS '19!

Patent issued: "Efficient sharing and compression expansion of data across processing systems" (10,497,084)

Interview with Knowledge@Wharton on machine programming.

Open source: AutoPerf (NeurIPS'19) has been released to the open source community.

Patent issued: "Methods and systems for performing a replay execution" (10,474,471)

Intel Newsroom Press Release on my team's Machine Programming Research.

Intel Division Recognition Award: "Outstanding Leadership of Machine Programming Patent Harvest"

Patent issued: "Compute optimization mechanism for deep neural networks" (10,417,734)

Patent issued: "Compute optimization mechanism for deep neural networks" (10,417,731)

Patent issued: "Autonomous machines through cloud, error corrections, and predictions" (10,410,115)

Accepted to NeurIPS: "A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions"

Opening address for Machine Programming Day @ Berkeley: "Intel's Machine Programming Pioneering Research Vision"

FORMER STUDENTS

MS advisor: Akhilesh Gupta, University of Pennsylvania -> Apple

MS advisor: Sam Weintraub, University of Pennsylvania -> Outrider

PhD committee member: Mohammad Mejbah ul Alam -> Intel Labs

PhD committee member: Wenjia Ruan, Lehigh University -> Qualcomm

PhD co-advisor: Irina Calciu, Brown University -> VMWare Research